Early-life exposure to air pollution alters resting-state functional connectivity patterns in late childhood

Early-life exposure to air pollution alters resting-state functional connectivity patterns in late childhood | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Early-life exposure to air pollution alters resting-state functional connectivity patterns in late childhood View ORCID Profile Snežana Todorović , View ORCID Profile Mikolaj Compa , View ORCID Profile Daniel S. Margulies , Emilia Rutkowska , View ORCID Profile Yarema Mysak , View ORCID Profile Malgorzata Lipowska , View ORCID Profile Katarzyna Sitnik-Warchulska , View ORCID Profile Bernadetta Izydorczyk , View ORCID Profile Katarzyna Kaczmarek-Majer , View ORCID Profile Krzysztof Skotak , Anna Degórska , View ORCID Profile Clemens Baumbach , View ORCID Profile James Grellier , View ORCID Profile Iana Markevych , View ORCID Profile Marcin Szwed doi: https://doi.org/10.1101/2025.11.16.25340328 Snežana Todorović 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Snežana Todorović For correspondence: snezana.todorovic{at}uj.edu.pl Mikolaj Compa 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mikolaj Compa Daniel S. Margulies 2 Centre National de la Recherche Scientifique, Université de Paris , INCC UMR 8002, Paris, France 3 The Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford , Oxford, United Kingdom, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel S. Margulies Emilia Rutkowska 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yarema Mysak 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yarema Mysak Malgorzata Lipowska 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Malgorzata Lipowska Katarzyna Sitnik-Warchulska 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katarzyna Sitnik-Warchulska Bernadetta Izydorczyk 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bernadetta Izydorczyk Katarzyna Kaczmarek-Majer 6 Institute of Environmental Protection-National Research Institute , Warsaw, Poland 7 Systems Research Institute, Polish Academy of Sciences , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katarzyna Kaczmarek-Majer Krzysztof Skotak 6 Institute of Environmental Protection-National Research Institute , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Krzysztof Skotak Anna Degórska 4 Institute of Psychology, University of Gdańsk , Gdańsk, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clemens Baumbach 1 Institute of Psychology, Jagiellonian University , Krakow, Poland 8 Institute and Clinic for Occupational, Social and Environmental Medicine, LMU University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Clemens Baumbach James Grellier 9 European Centre for Environment and Human Health, University of Exeter Medical School , Penryn, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James Grellier Iana Markevych 1 Institute of Psychology, Jagiellonian University , Krakow, Poland 10 Health and Quality of Life in a Green and Sustainable Environment Research Group, Strategic Research and Innovation Program for the Development of MU – Plovdiv, Medical University of Plovdiv , Plovdiv, Bulgaria 11 Environmental Health Division, Research Institute at Medical University of Plovdiv, Medical University of Plovdiv , Plovdiv, Bulgaria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Iana Markevych Marcin Szwed 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marcin Szwed Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Background Adverse environmental exposures such as air pollution can disrupt brain development, contributing to long-term risk of neurodevelopmental and psychiatric disorders. Resting-state functional connectivity (rsFC) captures large-scale brain alterations seen in such disorders, and growing evidence links air pollution exposure to adverse cognitive and mental health outcomes. Methods We used connectome-based predictive modeling (CPM), a data-driven approach that uses brain states to predict individual variables, in this case, air pollution exposures, to investigate whether individual patterns of rsFC at 10-13 years in children from the NeuroSmog study are associated with prior exposure to particulate matter (PM 10 ) and nitrogen dioxide (NO 2 ). The tested exposure periods comprised prenatal, from birth until the 2 nd birthday, 2 nd to 4 th birthday, the year prior to MRI data acquisition, and the week prior to MRI data acquisition. Results Functional connectivity at 10-13 years successfully predicted exposures to PM 10 in two subsequent early-life periods, from birth until the age of 2, and at the age of 2-4. Predictions for other exposure periods, as well as for all tested periods of exposure to NO 2 , were not significant. Higher PM 10 exposures in both periods were associated with a decrease in network segregation and heightened functional connectivity, in particular between the default mode, ventral attention, cerebellar, and salience networks. Conclusions Since segregation between brain networks normally decreases with age, our findings suggest that early-life exposure to PM 10 may disrupt this developmental trajectory. Exposures during a period of increased neuroplasticity can thus exert enduring effects on functional network architecture. Introduction The developing brain is particularly vulnerable to harmful environmental exposures, including air pollution ( 1 ), synthetic chemicals and psychosocial stressors ( 2 , 3 ). These adverse influences can have lasting consequences ( 4 – 6 ). Air pollution exposure, in particular, has been associated with increased psychiatric emergency visits in children ( 7 ) and increased dispensation of antipsychotics, sedatives, and sleep medications ( 8 ). Beyond its well-established impacts on respiratory and cardiovascular systems ( 9 , 10 ), air pollution may thus contribute to developmental and psychiatric disorders ( 2 ). Altered patterns of large-scale functional brain organization, as measured by resting-state functional connectivity (rsFC), are a consistent feature of psychiatric disorders ( 4 , 11 ). These alterations can be shared across diagnostic categories ( 12 ), or be disorder-specific, as observed in autism ( 13 ), anxiety, major depressive disorder ( 14 ), schizophrenia ( 13 ), bipolar disorder ( 14 ), obsessive-compulsive disorder ( 14 ), post-traumatic stress disorder ( 15 ), and attention-deficit/hyperactivity disorder (ADHD) ( 13 ). While prior research mostly focused on the consequences of disrupted rsFC, investigating causal mechanisms of environmental origins of such alterations would inform policy debates about air quality and standards and help advance screening strategies and interventions aimed at identified at-risk individuals. Mental disorders often emerge during childhood and adolescence ( 16 – 18 ). A consistent pattern of functional maturation in typically developing children includes increasing within-network specialization and selective long-range integration ( 19 – 21 ). The overall between-network segregation, for example, separation of the default mode network (DMN), engaged during internally oriented, self-referential thought ( 22 ) from sensory processing networks, increases with age. In parallel, networks supporting common processes, in particular the salience (SAL) and fronto-parietal (FP) network, become progressively more integrated ( 23 ). Epidemiological studies increasingly link air pollution to child mental health ( 7 , 8 ) cognitive ( 24 – 28 ) and brain ( 1 , 5 , 6 ) outcomes. However, findings of structural brain studies are inconsistent, with results that vary across exposure windows, imaging measures, and cohorts ( 1 , 29 , 30 ). The findings of functional MRI studies, by contrast, are more consistent. Higher exposure to air pollution has been linked to less segregated sensorimotor networks, stronger coupling between task-positive and task-negative systems, and longitudinally, to persistent reductions in amygdala connectivity with attentional, somatomotor, and auditory networks, as well as lasting alterations in salience–medioparietal connectivity ( 31 – 33 ). Together, these findings suggest that functional network reorganization may mediate the effects of air pollution on neurodevelopment. Here, we adopted connectome-based predictive modeling (CPM), a predictive, data-driven method developed to investigate associations between differences in the connectome and behavioral or clinical traits at the individual level ( 31 ). The predictive strength of CPM comes from its ability to identify networks of connections that reliably relate to the variable of interest within the sample, and to test how well these networks predict the variable in question in each of the participants (see Supplementary Materials). CPM has been used across diverse psychiatric and cognitive domains ( 32 – 41 ) to predict cognitive and clinical outcomes. Here, we applied CPM to test whether individual scores of exposure to particulate matter with aerodynamic diameter ≤10 μm (PM₁₀) and nitrogen dioxide (NO₂) could be associated with patterns of rsFC measured at ages 10-13. Specifically, we tested the the impact of early life exposure (prenatal, from birth to 2 years, and 2-4 years), a year preceding MRI data acquisition ( 42 ), as well of recent exposure, in the week prior to MRI data acquisition ( 24 ) (see Figure 1 ; for a previous study that addressed susceptibility windows of brain vulnerability to air pollution exposure, see ( 43 )). Based on prior evidence that early childhood represents a critical window of heightened neurodevelopmental sensitivity to environmental exposures ( 44 ), we hypothesized that exposure during the first four years of life would show the strongest associations with functional brain organization. Download figure Open in new tab Figure 1. Modeled periods of exposure for particulate matter with aerodynamic diameter ≤10 μm (PM₁₀) and nitrogen dioxide (NO₂). Given the high correlations (> .9) of NO 2 exposures over consecutive years, no model was created for NO₂ at 2-4 years. Photographs taken by the authors, modified with IA and reproduced with permission. Methods The analyses were pre-registered ( 45 ). The study was approved by the ethics committee of the Institute of Psychology, Jagiellonian University, Poland (#KE_24042019A). Written informed consent was obtained from participants and their legal guardians, as detailed in the protocol article( 46 ). Participants The Neurosmog study consists of 741 children, aged 10-13 years at the time of MRI scanning, that were recruited from 18 Polish towns selected based on air pollution levels and population size ( 46 ). Briefly, typically developing children were randomly selected from local schools (stratified random sampling), while children at risk for ADHD were recruited through convenience sampling via referrals or parental applications. ADHD diagnoses were based on ICD-11 criteria and a standardized test battery, applied by field clinical psychologists to all children, and independently verified by senior clinical psychologists. Exclusion criteria included major neurological, psychiatric, or developmental conditions, low birth weight, and insufficient proficiency in the Polish language. In the present study, children with available resting state fMRI data were considered for further analysis (N = 639), of which 636 provided complete demographic data. The application of further exclusion criteria (excessive movement, missing covariate measures, and missing air pollution exposure data) resulted in a final study sample of between 348 and 370 children depending on the air pollution exposure period (see “Air pollution exposure assessment”) ( Figure 2 ). Table 1 shows demographic data for the 370 children. Download figure Open in new tab Figure 2. rs-fMRI data preprocessing flowchart. View this table: View inline View popup Download powerpoint Table 1. Demographic characteristics of the participants included in the analysis. Age – age in years at the time of MRI data acquisition, Sex – as assigned at birth, Town size – large for towns with over 90 000 inhabitants, otherwise small. Air pollution exposure assessment Air pollution exposures to PM₁₀ and NO₂ were estimated at children’s home addresses using land use regression (LUR) models as detailed in prior work from our group( 24 , 29 , 47 ). Briefly, these models integrate ground monitoring data with meteorological information, chemical transport modeling, and land-use characteristics, allowing for high-resolution, spatially refined exposure estimates suitable for epidemiological studies ( 48 ). Short-term exposures, in contrast, were derived from European Environmental Agency (EEA) monitoring data, using mean pollutant concentrations from the background station closest to each child’s residence ( 24 ). Figure 1 shows time windows for which average air pollution concentration exposures were assigned: 2 nd and 3 rd trimester of fetal development, from birth to 2 nd birthday, from 2 nd to 4 th birthday, for the year and for the week prior to MRI data acquisition. Covariates For all exposure periods, the following covariates were included in the analysis: ( 1 ) child’s age at the time of MRI data acquisition (in months), ( 2 ) sex assigned at birth, ( 3 ) ADHD diagnosis, included to account for potential variability in functional connectivity related to clinical heterogeneity, and ( 4 ) school town type (large or small), included to account for potential differences in degree of urbanicity between smaller and larger towns. Socioeconomic status (SES) was not included as a primary covariate, as it did not correlate with air pollution exposure in our sample. Instead, SES, assessed as the minimum education level of the two parents, was examined in sensitivity analyses to assess the robustness of the observed associations. For prenatal and past-week exposure periods, an additional covariate was added, respectively birth season and time spent outdoors. Neuroimaging Data acquisition and preprocessing The neuroimaging data acquisition protocol is detailed elsewhere ( 46 ). In brief, the acquisition sequences for MRI and fMRI neuroimaging data were adopted from the Adolescent Brain Child Development (ABCD) study ( 49 ) and the Human Connectome Project - Development study ( 50 ). Figure 2 represents the rs-fMRI data preprocessing flowchart. Acquisition and preprocessing details are provided in Supplementary Materials. Functional connectivity matrices Single-subject whole brain rsFC matrices were calculated, using the CONN toolbox ( 51 ), between each pair of predefined functional regions of interest (ROI). 333 cortical ROI were selected from Gordon parcellation ( 52 ), 15 subcortical from FSL ( 53 ), and 26 cerebellar from the AAL ( 54 ). Thus, a total of 374 ROI was used and each participant’s functional connectivity matrix contained 374 × 374 = 139,876 values. Functional connectivity strengths were represented by Fisher-transformed bivariate correlation coefficients from a general linear model, estimated separately for each pair of ROI, characterizing the association between the time series of their blood-oxygenation-level–dependent (BOLD) signal fluctuations. Connectome-based predictive modeling (CPM) Separate CPM analyses were performed for each pollutant (PM₁₀, NO₂) and exposure window (prenatal, 0–2 years, 2–4 years, past year, and past week; see Figure 1 ). Because NO₂ exposure estimates were highly correlated across consecutive years (R² = 0.90–0.95, all p < .01), the 2-4-year window was excluded to avoid redundancy, resulting in nine models in total. Each CPM followed the same cross-validation procedure. First, the sample was divided into a training set, used to identify relevant connections and estimate model coefficients, and a test set, used to evaluate model performance. Within the training set, the strength of each functional connection was correlated with individual air pollution exposure levels using two-tailed Pearson correlations, computed using all participants with available data. Connections showing a significant association (p < 0.05) were grouped into two sets: one showing positive associations, where higher exposure was linked to stronger connectivity, and one showing negative associations, where higher exposure was linked to weaker connectivity. These two sets of selected connections were stored as masks, binary matrices marking which connections were retained for each association type. For each participant, in both training and test sets, their full connectivity matrix was reduced to a single positive and a single negative summary metric as follows. The connectivity matrix was multiplied by each mask, and the resulting connectivity values were summed and divided by two to avoid double-counting symmetric connections. This resulted in two scalar values per participant, one representing the overall strength of connections increasing with air pollution, and one for air pollution-related strength decrease. In the training set, these summary values, together with covariates, were entered into a linear regression with air pollution exposure as the outcome: where X denotes the participant’s summary connectivity strength, C represents the covariate matrix, and ŷ is the exposure value predicted from the model. The resulting coefficients were then applied to the test set to obtain predicted exposure values. CPM significance testing, further CPM analyses methods, and exploratory analyses of the impact of air pollution exposure on functional network segregation and integration are described in Supplementary Materials. Results Predictive Performance of CPM CPM performance metrics are shown in Table 2 . Between-subject differences in functional connectivity successfully predicted PM 10 exposure for the two early-life windows, from birth to age 2, and from age 2 to 4. In both periods, the values of PM 10 exposure predicted by CPM significantly correlated with the values in actual data (R = 0.10, p = 0.04, and R = 0.15, p = 0.01). Predictions were based on functional connections that positively correlated with PM 10 exposure; in other words, higher functional connectivity was predictive of higher exposures to PM 10 . View this table: View inline View popup Download powerpoint Table 2. Connectome-based predictive modeling (CPM) performance. Positive and negative associations of air pollution exposure during tested time periods with functional connectivity. Positive and negative associations here signify that higher air pollution is associated with either higher or lower functional connectivity in significant (predictive) connections, respectively. R represents the correlation coefficient between the actual air pollutant exposure for the given period and the air pollutant exposure predicted by the CPM based on resting state functional connectivity. p 1000 represents the empirical p-value for the null hypothesis R = 0 and is based on 1000 iterations of permutation testing. Statistically significant results are presented in bold. Distribution of predictive connections across functional networks The CPM model identified functional connections whose strength reliably contributes to predicting individual PM 10 exposure levels. We next examined which large-scale brain networks these predictive connections belonged to, as this reveals which systems are most affected by air pollution. Figure 3 shows predictive connections arranged by network and hemisphere. Table 3 reports the number and percentage of such predictive connections relative to all possible connections for each network. For both exposure periods (0-2 years and 2-4 years), associations were particularly evident in attentional systems, with both the dorsal and ventral attention networks (DAN, VAN) contributing alongside the default mode (DMN), cingulo-opercular (CON), cerebellar (CBL), sensorimotor (SM) and visual (VIS) networks. The networks mostly contributing to PM 10 prediction included salience (SAL), cerebellar (CBL) and sensorimotor networks (SM), with additional contributions from VAN at 2-4 years and on the subcortical network (SUB) at 0-2 years. Figure 4 shows the distribution of these effects. Download figure Open in new tab Figure 3. Resting state functional connections at the age 10-13 that significantly predict exposure to PM 10 a) from birth until the age of 2 (top) and b) from the second birthday until the age of 4 (bottom). Left: Regions of interest (ROI) grouped into functional networks and arranged symmetrically by hemisphere; the length of each segment corresponds to the number ROI belonging to that network in the predefined parcellation, which can differ slightly between hemispheres. Each curve represents a functional connection between two regions that is consistently associated with PM 10 exposure in the CPM model. To increase readability, only the functional connections of the top 25% of the most connected ROI are displayed. Right: Average percentage of connections impacted by exposure to PM 10 per network. See Supplementary Materials for details. DMN – default, CON – cingulo-opercular, DAN - dorsal attention, VAN – ventral attention, SAL – salience, FP – fronto-parietal, MP – medio-parietal, PO – parieto-occipital, SM – sensorimotor, VIS – visual, AUD – auditory, SUB – subcortical, CBL – cerebellar, None – unassigned. PM 10 : particulate matter with a diameter of 10 μm or less, CPM: connectome-based predictive modeling. Download figure Open in new tab Figure 4. Anatomical localization of regions impacted by exposure to PM 10 in each network a) from birth until the age of 2 (top) and b) between ages 2 and 4 (bottom). The colorbar represents each impacted regions’ degree of connectivity with other regions, i.e., the number of other regions it connects to. View this table: View inline View popup Table 3. Number of connections predictive of exposure to PM 10 per network. DMN – default, CON – cingulo-opercular, DAN – dorsal attention, VAN – ventral attention, SAL – salience, FP – fronto-parietal, MP – medio-parietal, PO – parieto-occipital, SM – sensorimotor, VIS – visual, AUD – auditory, SUB – subcortical, CBL – cerebellar, None – unassigned. PM 10 – particulate matter with a diameter of 10 μm or less. Direction and strength of affected functional connections The detected “positive” relationship between exposure to PM 10 at 0-2 and at 2-4 years and functional connectivity values signifies that higher exposure is associated with an increase in functional connectivity values. Since for positive functional connectivity values an increase means the strengthening of the connections, and for negative functional connectivity values it means the weakening, as the negative values become closer to zero, we examined separately the positive and the negative functional connectivity values associated with increased PM 10 exposure. As Figure 5 shows, these affected connections had predominantly positive connectivity values (median: 0.08, range: -0.31 to 0.62), suggesting weakened functional boundaries between networks and decreased segregation. While both positive and negative connections were predictive of PM 10 exposure, positive connections had on average higher values. Download figure Open in new tab Figure 5. Average values of functional connections that significantly predicted PM₁₀ exposure. Left panels display subnetworks for which mean functional connectivity was positive and increased with higher PM₁₀. Right panels display subnetworks for which mean negative functional connections weakened (i.e., became less negative) with higher PM₁₀. Gray cells indicate subnetwork pairs with no suprathreshold predictive edges. The opposite association patterns (positive connectivity decreasing with PM₁₀ or negative connectivity becoming more negative) were not significant in the CPM analysis. Top row: exposure from birth until age 2; bottom row: exposure from age 2 to 4. DMN – default, CON – cingulo-opercular, DAN – dorsal attention, VAN – ventral attention, SAL – salience, FP – fronto-parietal, MP – medio-parietal, PO – parieto-occipital, SM – sensorimotor, VIS – visual, AUD – auditory, SUB – subcortical, CBL – cerebellar, None – unassigned, PM 10 – particulate matter with a diameter of 10 μm or less, CPM – connectome-based predictive modeling. Network segregation We conducted an exploratory post hoc analysis of segregation metrics for the most predictive period of PM₁₀ exposure, between ages 2 and 4 (see Supplementary Materials). Reduced segregation generally indicates less mature network organization. Higher exposure was linked to lower segregation in the default mode network (DMN), ventral attention network (VAN), and cingulo-opercular network (CON), and to a lesser extent in the parietal and visual networks (Supplementary Table 1); only DMN remained significant after correction (p < 0.001). Sensitivity Analysis The results remained unchanged in a follow-up sensitivity analysis, where the calculations were repeated including SES, living in a single-parent or two-parent household, early life exposure to smoking, and maternal alcohol use during pregnancy as additional covariates. For both 0-2 years and 2-4 years periods the R values remained the same to the second decimal (R = 0.10 and 0.15) and p-values either remained the same or decreased by .01 (see Supplementary Table 2). Discussion Consistent with prior reports of air pollution-related alterations in the brain development ( 23 , 42 , 55 ), we found that resting-state functional connectivity at ages 10-13 predicted individual levels of PM₁₀ exposure, with performances comparable to previous CPM applications ( 31 ) ( Table 2 ). Our main hypothesis, that exposure between birth and age four would be most predictive, was confirmed for PM₁₀, with significant results in both the 0-2 and 2-4 year windows. Prenatal and more recent exposures were not predictive, and no associations were observed for NO₂. Early-life exposure to PM₁₀ was mainly predicted by positive functional connectivity, which tended to increase with higher exposures ( Figure 3 ). These predictive patterns primarily involved the default mode network (DMN), a system engaged during internally oriented, self-referential thought and typically suppressed during externally focused tasks ( 22 ); the salience (SAL) network, responsible for detecting behaviorally relevant stimuli ( 56 ); and the dorsal (DAN) and ventral attention networks (VAN) ( 57 , 58 ), the cingulo-opercular network (CON) ( 59 , 60 ), as well as the fronto-parietal network (FP) ( 60 ). In particular, higher PM₁₀ exposure increased DMN–FP coupling, both in strength ( Figure 4 ) and in extent of affected connections ( Figure 3 , right), consistent with previous reports of DMN-to-FP hyperconnectivity associated with air pollution ( 23 ). A post-hoc segregation/integration analysis suggested that increases in predicted air pollution are associated with decreased segregation of large-scale networks. This might indicate less mature network organization i.e. that higher exposure may alter the differentiation of higher-order systems developing in early childhood. This interpretation is consistent with prior work linking air pollution to less segregated attentional and sensorimotor networks ( 1 ). Our findings support the idea that early-life PM₁₀ exposure disrupts the normative trajectory of increasing network specialization. Developmental slowing vs. disruptive plasticity In normative development, primary networks such as the visual system mature early, whereas higher-order systems like DAN and DMN continue to refine into adolescence ( 61 – 63 ), stabilizing around age 12 as networks shift from within-network cohesion toward greater between-network integration ( 63 , 64 ). The alterations associated with exposure to PM₁₀ we observed may reflect a range of possible developmental trajectories, including slower maturation, premature integration, or alternative reorganizations, rather than a uniform delay. The salience network (SAL) was particularly affected in the 2-4 year exposure window, showing high proportions of predictive connections, and notably increased connectivity with DMN and FP. These networks constitute the core “triple network” in the pathophysiology of psychiatric illness. In this framework, deficits in engagement and disengagement of these networks, reflected in their altered connectivity, can account for several psychiatric disorders, in particular for psychopathologies with disturbances of cognition and the self ( 64 ). Longitudinal studies report abuse-related within-network connectivity increases, such as neglect-related stronger SAL-DMN coupling ( 65 – 68 ), and similar SAL-DMN-executive shifts linked to other adversities such as unpredictability, food insecurity, and hostile environments ( 69 – 71 ). Comparably, PM₁₀-related increases within VAN, subcortical, and visual networks mirror findings in children with ADHD( 72 ) and early-life psychosocial adversity ( 73 – 77 ), suggesting that air pollution may constitute an environmental adversity with shared neurobiological pathways, such as neuroinflammation ( 78 , 79 ) hypothalamic-pituitary-adrenal axis dysregulation ( 80 ), and lasting implications for cognitive control and mental health. Air pollution exposure as risk factor for neurodevelopmental and psychiatric conditions Reduced segregation and atypical integration within default mode, salience, and attention networks have been reported in ADHD ( 81 , 82 ), ASD ( 83 ), and schizophrenia ( 81 ), and exposure to PM₁₀ and other air pollutants has been associated with increased incidence of ADHD symptoms ( 84 , 85 ). Prominent long-term impact of early-life PM₁₀ exposure on salience connectivity is of particular interest given the role of salience dysfunction across psychiatric conditions ( 86 ) and links between ambient air pollution and increased pediatric psychiatric emergency visits ( 7 ). Our findings support the idea that early environmental exposures may contribute to risk trajectories via alterations in core cognitive-control networks. Strengths and limitations Our study has several strengths. To the best of our knowledge, it is one of the first applications of CPM to predict environmental exposures (see ( 87 ) for a recently published CPM study on exposure to PM 2.5 in the ABCD cohort). We examined multiple early-life windows in a large, well-characterized pediatric sample. Additional strengths include high-resolution state-of-the-art assessments of air pollution exposure in a region with elevated pollution levels. Nevertheless, several limitations should be acknowledged. The current study was cross-sectional. After splitting our sample in with and without ADHD groups, the sample sizes became too small for independent CPM analyses or between-group CPM cross-validation. Instead, we used one single subject group and included ADHD as a covariate in the CPM, which did not allow quantification of its contributions to the model. Conclusion In summary, we found that early-life PM₁₀ exposure during specific developmental windows is associated with lasting differences in large-scale functional brain networks, particularly in systems supporting attention, salience, and executive control. Our results demonstrate the usefulness of predictive approaches to exposure studies, and contribute to a growing body of evidence linking environmental exposures to neurodevelopmental alterations that may increase vulnerability to psychiatric disorders. Data Availability All data produced in the present study are available upon reasonable request to the authors Code availability The scripts used connectome-based predictive modeling (CPM) are available at https://github.com/snezanatodorovic/CPM_CONN_MATLAB . The repository includes code and documentation sufficient to reproduce the main analyses presented in this study. Acknowledgments This work was supported by “NeuroSmog: Determining the impact of air pollution on the developing brain” (Nr. POIR.04.04.00–1763/18–00) grant to M.S. implemented as part of the TEAM-NET programme of the Foundation for Polish Science, co-financed from EU resources obtained from the European Regional Development Fund under the Smart Growth Operational Programme, a grant from the Priority Research Area “Anthropocene” under the Strategic Programme Excellence Initiative at Jagiellonian University and the Polish National Science Centre grant no. K/NCN/000514 to M.S; I.M. was supported by the National Science Centre, Poland (grant number 2023/50/E/HS6/00102) and the “Strategic research and innovation program for the development of Medical University – Plovdiv” No. BG-RRP-2.004-0007-C01. We are very grateful to all of the children and their families for their participation in the study, to partner schools for helping us reach out to children assigned to the control group, to field psychologists for identifying children with ADHD and performing psychological testing on all children, to the study team for technical, logistic, administrative and communication efforts, and in particular to Mariola Piotrowska for invaluable organizational support throughout the project. References 1. ↵ Morrel J , Dong M , Rosario MA , Cotter DL , Bottenhorn KL , Herting MM ( 2025 ): A systematic review of air pollution exposure and brain structure and function during development . Environmental Research 275 : 121368 . OpenUrl 2. ↵ Nigg JT ( 2023 ): Considerations toward an epigenetic and common pathways theory of mental disorder . Journal of Psychopathology and Clinical Science 132 : 297 – 313 . OpenUrl 3. ↵ Vermeulen R , Schymanski EL , Barabási A-L , Miller GW ( 2020 ) : The exposome and health: Where chemistry meets biology . Science 367 : 392 – 396 . OpenUrl Abstract / FREE Full Text 4. ↵ Thomason ME ( 2020 ) : Development of Brain Networks In Utero: Relevance for Common Neural Disorders . Biological Psychiatry 88 : 40 – 50 . OpenUrl CrossRef PubMed 5. ↵ Cory-Slechta DA , Merrill A , Sobolewski M ( 2023 ): Air Pollution–Related Neurotoxicity Across the Life Span . Annu Rev Pharmacol Toxicol 63 : 143 – 163 . OpenUrl CrossRef PubMed 6. ↵ Polemiti E , Hese S , Schepanski K , Yuan J , Schumann G ( 2024 ): How does the macroenvironment influence brain and behaviour—a review of current status and future perspectives . Mol Psychiatry 29 : 3268 – 3286 . OpenUrl PubMed 7. ↵ Brokamp C , Strawn JR , Beck AF , Ryan P ( 2019 ) : Pediatric Psychiatric Emergency Department Utilization and Fine Particulate Matter: A Case-Crossover Study . Environ Health Perspect 127 : 097006 . OpenUrl CrossRef PubMed 8. ↵ Oudin A , Bråbäck L , Åström DO , Strömgren M , Forsberg B ( 2016 ): Association between neighbourhood air pollution concentrations and dispensed medication for psychiatric disorders in a large longitudinal cohort of Swedish children and adolescents . BMJ open 6 : e010004 . OpenUrl Abstract / FREE Full Text 9. ↵ Brauer M , Roth GA , Aravkin AY , Zheng P , Abate KH , Abate YH , et al. ( 2024 ): Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021 . The Lancet 403 : 2162 – 2203 . OpenUrl 10. ↵ Boogaard H , Patton AP , Atkinson RW , Brook JR , Chang HH , Crouse DL , et al. ( 2022 ) : Long-term exposure to traffic-related air pollution and selected health outcomes: A systematic review and meta-analysis . Environment International 164 : 107262 . OpenUrl CrossRef PubMed 11. ↵ Woodward ND , Cascio CJ ( 2015 ): Resting-State Functional Connectivity in Psychiatric Disorders . JAMA Psychiatry 72 : 743 – 744 . OpenUrl PubMed 12. ↵ Wheelock MD , Lean RE , Bora S , Melzer TR , Eggebrecht AT , Smyser CD , Woodward LJ ( 2021 ): Functional Connectivity Network Disruption Underlies Domain-Specific Impairments in Attention for Children Born Very Preterm . Cerebral Cortex 31 : 1383 – 1394 . OpenUrl CrossRef PubMed 13. ↵ Posner MI , Rothbart MK , Voelker P ( 2016 ): Developing brain networks of attention . Current Opinion in Pediatrics 28 : 720 – 724 . OpenUrl PubMed 14. ↵ Lees B , Squeglia LM , McTeague LM , Forbes MK , Krueger RF , Sunderland M , et al. ( 2021 ) : Altered Neurocognitive Functional Connectivity and Activation Patterns Underlie Psychopathology in Preadolescence . Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 6 : 387 – 398 . OpenUrl 15. ↵ Korgaonkar MS , Chakouch C , Breukelaar IA , Erlinger M , Felmingham KL , Forbes D , et al. ( 2020 ): Intrinsic connectomes underlying response to trauma-focused psychotherapy in post-traumatic stress disorder . Transl Psychiatry 10 : 1 – 11 . OpenUrl PubMed 16. ↵ Tan C , Frewer V , Cox G , Williams K , Ure A ( 2021 ) : Prevalence and Age of Onset of Regression in Children with Autism Spectrum Disorder: A Systematic Review and Meta-analytical Update . Autism Research 14 : 582 – 598 . OpenUrl PubMed 17. Rocco I , Corso B , Bonati M , Minicuci N ( 2021 ): Time of onset and/or diagnosis of ADHD in European children: a systematic review . BMC Psychiatry 21 : 575 . OpenUrl CrossRef PubMed 18. ↵ Kieling C , Kieling RR , Rohde LA , Frick PJ , Moffitt T , Nigg JT , et al. ( 2010 ): The Age at Onset of Attention Deficit Hyperactivity Disorder . AJP 167 : 14 – 16 . OpenUrl 19. ↵ Grayson DS , Fair DA ( 2017 ) : Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature . NeuroImage 160 : 15 – 31 . OpenUrl CrossRef PubMed 20. Fair DA , Cohen AL , Power JD , Dosenbach NUF , Church JA , Miezin FM , et al. ( 2009 ): Functional Brain Networks Develop from a “Local to Distributed” Organization . PLOS Computational Biology 5 : e1000381 . OpenUrl 21. ↵ Power JD , Cohen AL , Nelson SM , Wig GS , Barnes KA , Church JA , et al. ( 2011 ): Functional Network Organization of the Human Brain . Neuron 72 : 665 – 678 . OpenUrl CrossRef PubMed Web of Science 22. ↵ Menon V ( 2023 ): 20 years of the default mode network: A review and synthesis . Neuron 111 : 2469 – 2487 . OpenUrl CrossRef PubMed 23. ↵ Cotter DL , Campbell CE , Sukumaran K , McConnell R , Berhane K , Schwartz J , et al. ( 2023 ): Effects of ambient fine particulates, nitrogen dioxide, and ozone on maturation of functional brain networks across early adolescence . Environment International 177 : 108001 . OpenUrl CrossRef PubMed 24. ↵ Compa M , Baumbach C , Kaczmarek-Majer K , Buczyłowska D , Gradys GO , Skotak K , et al. ( 2023 ): Air pollution and attention in Polish schoolchildren with and without ADHD . Science of The Total Environment 892 : 164759 . OpenUrl PubMed 25. Thompson R , Smith RB , Karim YB , Shen C , Drummond K , Teng C , Toledano MB ( 2023 ) : Air pollution and human cognition: A systematic review and meta-analysis . Science of The Total Environment 859 : 160234 . OpenUrl CrossRef PubMed 26. Zhang X , Chen X , Zhang X ( 2018 ): The impact of exposure to air pollution on cognitive performance . Proceedings of the National Academy of Sciences of the United States of America 115 : 9193 – 9197 . OpenUrl Abstract / FREE Full Text 27. Schikowski T , Altuğ H ( 2020 ): The role of air pollution in cognitive impairment and decline . Neurochemistry International 136 : 104708 . OpenUrl PubMed 28. ↵ Clifford A , Lang L , Chen R , Anstey KJ , Seaton A ( 2016 ): Exposure to air pollution and cognitive functioning across the life course - A systematic literature review . Environmental Research 147 : 383 – 398 . OpenUrl CrossRef PubMed 29. ↵ Szwed M , de Jesus AV , Kossowski B , Ahmadi H , Rutkowska E , Mysak Y , et al. ( 2024 , September 12): Air Pollution and Cortical Myelin T1w/T2w Ratio Estimates in School-Age Children from the Abcd and Neurosmog Studies [SSRN Scholarly Paper no. 4942860] . Rochester, NY : Social Science Research Network . doi: 10.2139/ssrn.4942860 OpenUrl CrossRef 30. ↵ Lewandowska , P , Bajada , C , Mysak Y , Domagalik A , Kossowski B , Baumbach C , et al. ( 2025 ): The impact of early life exposure to air pollution on the brain: a diffusion MRI study in 10–13-year-old children with and without ADHD diagnosis . Human Brain Mapping . doi: 10.1002/hbm.70306 OpenUrl CrossRef 31. ↵ Shen X , Finn ES , Scheinost D , Rosenberg MD , Chun MM , Papademetris X , Constable RT ( 2017 ): Using connectome-based predictive modeling to predict individual behavior from brain connectivity . Nat Protoc 12 : 506 – 518 . OpenUrl CrossRef PubMed 32. ↵ Ge R , Humaira A , Gregory E , Alamian G , MacMillan EL , Barlow L , et al. ( 2022 ) : Predictive Value of Acute Neuroplastic Response to rTMS in Treatment Outcome in Depression: A Concurrent TMS-fMRI Trial . AJP 179 : 500 – 508 . OpenUrl 33. ↵ Gao M , Wong NML , Lin C , Huang C-M , Liu H-L , Toh C-H , et al. ( 2023 ): Multimodal brain connectome-based prediction of suicide risk in people with late-life depression . Nat Mental Health 1 : 100 – 113 . OpenUrl 34. Hsu W-T , Rosenberg MD , Scheinost D , Constable RT , Chun MM ( 2018 ): Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals . Social Cognitive and Affective Neuroscience 13 : 224 – 232 . OpenUrl CrossRef PubMed 35. Feng Q , Ren Z , Wei D , Liu C , Wang X , Li X , et al. ( 2024 ): Connectome-based predictive modeling of Internet addiction symptomatology . Social Cognitive and Affective Neuroscience 19 : nsae007 . OpenUrl PubMed 36. Antons S , Yip SW , Lacadie CM , Dadashkarimi J , Scheinost D , Brand M , Potenza MN ( 2023 ): Connectome-based prediction of craving in gambling disorder and cocaine use disorder . Dialogues in Clinical Neuroscience 25 : 33 – 42 . OpenUrl CrossRef PubMed 37. Horien C , Floris DL , Greene AS , Noble S , Rolison M , Tejavibulya L , et al. ( 2022 ): Functional Connectome–Based Predictive Modeling in Autism . Biological Psychiatry 92 : 626 – 642 . OpenUrl PubMed 38. Goldfarb EV , Rosenberg MD , Seo D , Constable RT , Sinha R ( 2020 ): Hippocampal seed connectome-based modeling predicts the feeling of stress . Nat Commun 11 : 2650 . OpenUrl CrossRef PubMed 39. Wang Z , Goerlich KS , Ai H , Aleman A , Luo Y , Xu P ( 2021 ): Connectome-Based Predictive Modeling of Individual Anxiety . Cerebral Cortex 31 : 3006 – 3020 . OpenUrl CrossRef PubMed 40. Lin Q , Rosenberg MD , Yoo K , Hsu TW , O’Connell TP , Chun MM ( 2018 ): Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer’s Disease . Front Aging Neurosci 10 . doi: 10.3389/fnagi.2018.00094 OpenUrl CrossRef 41. ↵ Barron DS , Gao S , Dadashkarimi J , Greene AS , Spann MN , Noble S , et al. ( 2021 ): Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders . Cerebral Cortex 31 : 2523 – 2533 . OpenUrl CrossRef PubMed 42. ↵ Perez-Crespo L , Kusters MSW , López-Vicente M , Lubczyńska MJ , Foraster M , White T , et al. ( 2022 ): Exposure to traffic-related air pollution and noise during pregnancy and childhood, and functional brain connectivity in preadolescents . Environment International 164 : 107275 . OpenUrl CrossRef PubMed 43. ↵ Binter AC , Kusters MSW , van den Dries MA , Alonso L , Lubczyńska MJ , Hoek G , et al. ( 2022 ) : Air pollution, white matter microstructure, and brain volumes: Periods of susceptibility from pregnancy to preadolescence . Environmental Pollution 313 . doi: 10.1016/j.envpol.2022.120109 OpenUrl CrossRef PubMed 44. ↵ Morrel J , Dong M , Rosario MA , Cotter DL , Bottenhorn KL , Herting MM ( 2024 , September 14): A Systematic Review of Air Pollution Exposure and Brain Structure and Function during Development . doi: 10.1101/2024.09.13.24313629 OpenUrl Abstract / FREE Full Text 45. ↵ Todorović S ( 2024 ): Resting-state fMRI analysis of short-term and early-life air pollution exposure effects on developing brains in children from the Neurosmog cohort . OSF Registries . doi: 10.17605/OSF.IO/J6K3E OpenUrl CrossRef 46. ↵ Markevych I , Orlov N , Grellier J , Kaczmarek-Majer K , Lipowska M , Sitnik-Warchulska K , et al. ( 2022 ) : Neurosmog: Determining the impact of air pollution on the developing brain: Project protocol . International Journal of Environmental Research and Public Health 19 . doi: 10.3390/ijerph19010310 OpenUrl CrossRef 47. ↵ Fraszczyk M , Kaczmarek-Majer K , Hryniewicz O , Skotak K , Degórska A ( 2022 ): Expert-in-the-loop Stepwise Regression and its Application in Air Pollution Modeling . 2022 IEEE 11th International Conference on Intelligent Systems (IS) 1 – 7 . 48. ↵ De Hoogh K , Gulliver J , Donkelaar AV , Martin RV , Marshall JD , Bechle MJ , et al. ( 2016 ): Development of West-European PM 2.5 and NO 2 land use regression models incorporating satellite-derived and chemical transport modelling data . Environmental Research 151 : 1 – 10 . OpenUrl 49. ↵ Casey BJ , Cannonier T , Conley MI , Cohen AO , Barch DM , Heitzeg MM , et al. ( 2018 ) : The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites . Developmental Cognitive Neuroscience 32 : 43 – 54 . OpenUrl PubMed 50. ↵ Harms MP , Somerville LH , Ances BM , Andersson J , Barch DM , Bastiani M , et al. ( 2018 ) : Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects . NeuroImage 183 : 972 – 984 . OpenUrl CrossRef PubMed 51. ↵ CONN (n.d.): CONN: functional connectivity toolbox . Retrieved from https://www.nitrc.org/projects/conn 52. ↵ Gordon EM , Laumann TO , Adeyemo B , Huckins JF , Kelley WM , Petersen SE ( 2016 ): Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations . Cerebral Cortex 26 : 288 – 303 . OpenUrl CrossRef PubMed 53. ↵ Patenaude B , Smith SM , Kennedy DN , Jenkinson M ( 2011 ): A Bayesian model of shape and appearance for subcortical brain segmentation . NeuroImage 56 : 907 – 922 . OpenUrl CrossRef PubMed Web of Science 54. ↵ Tzourio-Mazoyer N , Landeau B , Papathanassiou D , Crivello F , Etard O , Delcroix N , et al. ( 2002 ): Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain . NeuroImage 15 : 273 – 289 . OpenUrl CrossRef PubMed Web of Science 55. ↵ López-Vicente M , Kusters MSW , Petricola S , Tiemeier H , Muetzel RL , Guxens M ( 2025 ): Short-term exposure to traffic-related air pollution and dynamic brain connectivity in adolescents . Developmental Cognitive Neuroscience 74 : 101574 . OpenUrl PubMed 56. ↵ Seeley WW , Menon V , Schatzberg AF , Keller J , Glover GH , Kenna H , et al. ( 2007 ): Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control . J Neurosci 27 : 2349 – 2356 . OpenUrl Abstract / FREE Full Text 57. ↵ Ahrens M-M , Veniero D , Freund IM , Harvey M , Thut G ( 2019 ): Both dorsal and ventral attention network nodes are implicated in exogenously driven visuospatial anticipation . Cortex 117 : 168 – 181 . OpenUrl CrossRef PubMed 58. ↵ Dong H-M , Zhang X-H , Labache L , Zhang S , Ooi LQR , Yeo BTT , et al. ( 2024 ): Ventral attention network connectivity is linked to cortical maturation and cognitive ability in childhood . Nat Neurosci 27 : 2009 – 2020 . OpenUrl CrossRef PubMed 59. ↵ Dosenbach NUF , Visscher KM , Palmer ED , Miezin FM , Wenger KK , Kang HC , et al. ( 2006 ): A Core System for the Implementation of Task Sets . Neuron 50 : 799 – 812 . OpenUrl CrossRef PubMed Web of Science 60. ↵ Marek S , Dosenbach NUF ( 2018 ): The frontoparietal network: function, electrophysiology, and importance of individual precision mapping . Dialogues in Clinical Neuroscience 20 : 133 – 140 . OpenUrl CrossRef PubMed 61. ↵ Zhang H , Shen D , Lin W ( 2019 ) : Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts . NeuroImage 185 : 664 – 684 . OpenUrl CrossRef PubMed 62. Uddin LQ ( 2010 ): Typical and atypical development of functional human brain networks: insights from resting-state fMRI . Front Syst Neurosci 4 . doi: 10.3389/fnsys.2010.00021 OpenUrl CrossRef PubMed 63. ↵ Dufford AJ , Noble S , Gao S , Scheinost D ( 2021 ): The instability of functional connectomes across the first year of life . Developmental Cognitive Neuroscience 51 : 101007 . OpenUrl PubMed 64. ↵ Menon V ( 2011 ): Large-scale brain networks and psychopathology: a unifying triple network model . Trends in Cognitive Sciences 15 : 483 – 506 . OpenUrl CrossRef PubMed Web of Science 65. ↵ Rakesh D , Allen NB , Whittle S ( 2023 ): Longitudinal changes in within-salience network functional connectivity mediate the relationship between childhood abuse and neglect, and mental health during adolescence . Psychological Medicine 53 : 1552 – 1564 . OpenUrl CrossRef PubMed 66. Lindenmuth M , Chen Y-Y , Lee T-H , Melville N , Casas B , Kim-Spoon J ( 2025 ): A six-year longitudinal study examining chronicity and timing of maltreatment effects on risk-related functional connectivity change across adolescence . Developmental Cognitive Neuroscience 74 : 101577 . OpenUrl PubMed 67. Fadel E , Boeker H , Gaertner M , Richter A , Kleim B , Seifritz E , et al. ( 2021 ): Differential Alterations in Resting State Functional Connectivity Associated with Depressive Symptoms and Early Life Adversity . Brain Sciences 11 : 591 . OpenUrl PubMed 68. ↵ Gerin MI , Viding E , Herringa RJ , Russell JD , McCrory EJ ( 2023 ): A systematic review of childhood maltreatment and resting state functional connectivity . Developmental Cognitive Neuroscience 64 : 101322 . OpenUrl PubMed 69. ↵ Chahal R , Miller JG , Yuan JP , Buthmann JL , Gotlib IH ( 2022 ) : An exploration of dimensions of early adversity and the development of functional brain network connectivity during adolescence: Implications for trajectories of internalizing symptoms . Development and Psychopathology 34 : 557 – 571 . OpenUrl CrossRef PubMed 70. Reck A , Sweet LH , Geier C , Kogan SM , Cui Z , Oshri A ( 2024 ) : Food insecurity and adolescent impulsivity: The mediating role of functional connectivity in the context of family flexibility . Developmental Science 27 : e13554 . OpenUrl CrossRef PubMed 71. ↵ Son JJ , Rice DL , Schantell M , Picci G , Okelberry HJ , Coutant AT , et al. ( 2025 ): Social pain is associated with altered developmental trajectories of connectivity among the triple network model of psychopathology . Social Cognitive and Affective Neuroscience 20 : nsaf037. 72. ↵ Merzon L , Tauriainen S , Triana A , Nurmi T , Huhdanpää H , Mannerkoski M , et al. ( 2025 ): Real-world goal-directed behavior reveals aberrant functional brain connectivity in children with ADHD . PLOS ONE 20 : e0319746 . OpenUrl PubMed 73. ↵ Herringa RJ , Burghy CA , Stodola DE , Fox ME , Davidson RJ , Essex MJ ( 2016 ) : Enhanced Prefrontal-Amygdala Connectivity Following Childhood Adversity as a Protective Mechanism Against Internalizing in Adolescence . Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 1 : 326 – 334 . OpenUrl 74. Gerin MI , Viding E , Puetz VB , Armbruster-Genc DJN , Rankin G , McCrory EJ ( 2024 ): Atypical Interpersonal Problem-Solving and Resting-state Functional Connectivity in Adolescents with Maltreatment Experience . Current Neuropharmacology 22 : 290 – 301 . OpenUrl PubMed 75. White JD , Arefin TM , Pugliese A , Lee CH , Gassen J , Zhang J , Kaffman A ( 2020 , December 1): Early life stress causes sex-specific changes in adult fronto-limbic connectivity that differentially drive learning . eLife . doi: 10.7554/eLife.58301 OpenUrl CrossRef PubMed 76. Miller JG , Ho TC , Humphreys KL , King LS , Foland-Ross LC , Colich NL , et al. ( 2020 ) : Early Life Stress, Frontoamygdala Connectivity, and Biological Aging in Adolescence: A Longitudinal Investigation . Cerebral Cortex 30 : 4269 – 4280 . OpenUrl CrossRef PubMed 77. ↵ Silvers JA , Lumian DS , Gabard-Durnam L , Gee DG , Goff B , Fareri DS , et al. ( 2016 ): Previous Institutionalization Is Followed by Broader Amygdala–Hippocampal–PFC Network Connectivity during Aversive Learning in Human Development . J Neurosci 36 : 6420 – 6430 . OpenUrl Abstract / FREE Full Text 78. ↵ Herting MM , Bottenhorn KL , Cotter DL ( 2024 ): Outdoor air pollution and brain development in childhood and adolescence . Trends in Neurosciences 47 : 593 – 607 . OpenUrl CrossRef PubMed 79. ↵ Calcia MA , Bonsall DR , Bloomfield PS , Selvaraj S , Barichello T , Howes OD ( 2016 ): Stress and neuroinflammation: a systematic review of the effects of stress on microglia and the implications for mental illness . Psychopharmacology 233 : 1637 – 1650 . OpenUrl CrossRef PubMed 80. ↵ McEwen BS ( 2007 ) : Physiology and Neurobiology of Stress and Adaptation: Central Role of the Brain . Physiological Reviews 87 : 873 – 904 . OpenUrl CrossRef PubMed Web of Science 81. ↵ Yang GJ , Murray JD , Wang X-J , Glahn DC , Pearlson GD , Repovs G , et al. ( 2016 ): Functional hierarchy underlies preferential connectivity disturbances in schizophrenia . Proc Natl Acad Sci USA 113 . doi: 10.1073/pnas.1508436113 OpenUrl Abstract / FREE Full Text 82. ↵ Liu N , Liu Q , Yang Z , Xu J , Fu G , Zhou Y , et al. ( 2023 ) : Different functional alteration in attention-deficit/hyperactivity disorder across developmental age groups: A meta-analysis and an independent validation of resting-state functional connectivity studies . CNS Neurosci Ther 29 : 60 – 69 . OpenUrl CrossRef PubMed 83. ↵ Uddin LQ , Supekar K , Menon V ( 2013 ): Reconceptualizing functional brain connectivity in autism from a developmental perspective . Front Hum Neurosci 7 . doi: 10.3389/fnhum.2013.00458 OpenUrl CrossRef 84. ↵ Yang B-Y , Zeng X-W , Markevych I , Bloom MS , Heinrich J , Knibbs LD , et al. ( 2019 ): Association Between Greenness Surrounding Schools and Kindergartens and Attention-Deficit/Hyperactivity Disorder in Children in China . JAMA Netw Open 2 : e1917862 . OpenUrl 85. ↵ Ahmad S , K G N , Mani Babu A , Ranjan R , Kumar P ( 2024 ) : Association Between Ambient Air Pollution and Attention-Deficit/Hyperactivity Disorder (ADHD) in Children: A Systematic Review and Meta-Analysis . Cureus . doi: 10.7759/cureus.71527 OpenUrl CrossRef 86. ↵ Peters SK , Dunlop K , Downar J ( 2016 ) : Cortico-Striatal-Thalamic Loop Circuits of the Salience Network: A Central Pathway in Psychiatric Disease and Treatment . Front Syst Neurosci 10 . doi: 10.3389/fnsys.2016.00104 OpenUrl CrossRef PubMed 87. ↵ Bottenhorn KL , Sukumaran K , Corbett JD , De Jesus AV , Cardenas-Iniguez C , Habre R , et al. ( 2025 , September 25): Sources of outdoor air pollution exposure and child brain network development across the United States . doi: 10.1101/2025.09.23.677862 OpenUrl Abstract / FREE Full Text 88. Whitfield-Gabrieli S , Nieto-Castanon A ( 2012 ) : Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks . Brain Connectivity 2 : 125 – 141 . OpenUrl PubMed 89. Jenkinson M , Bannister P , Brady M , Smith S ( 2002 ): Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images . NeuroImage 17 : 825 – 841 . OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted November 17, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Early-life exposure to air pollution alters resting-state functional connectivity patterns in late childhood Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Early-life exposure to air pollution alters resting-state functional connectivity patterns in late childhood Snežana Todorović , Mikolaj Compa , Daniel S. Margulies , Emilia Rutkowska , Yarema Mysak , Malgorzata Lipowska , Katarzyna Sitnik-Warchulska , Bernadetta Izydorczyk , Katarzyna Kaczmarek-Majer , Krzysztof Skotak , Anna Degórska , Clemens Baumbach , James Grellier , Iana Markevych , Marcin Szwed medRxiv 2025.11.16.25340328; doi: https://doi.org/10.1101/2025.11.16.25340328 Share This Article: Copy Citation Tools Early-life exposure to air pollution alters resting-state functional connectivity patterns in late childhood Snežana Todorović , Mikolaj Compa , Daniel S. Margulies , Emilia Rutkowska , Yarema Mysak , Malgorzata Lipowska , Katarzyna Sitnik-Warchulska , Bernadetta Izydorczyk , Katarzyna Kaczmarek-Majer , Krzysztof Skotak , Anna Degórska , Clemens Baumbach , James Grellier , Iana Markevych , Marcin Szwed medRxiv 2025.11.16.25340328; doi: https://doi.org/10.1101/2025.11.16.25340328 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (570) Allergy and Immunology (863) Anesthesia (301) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1511) Epidemiology (15231) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6610) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15924) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6608) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3338) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5450) Public and Global Health (9240) Radiology and Imaging (2203) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a020bd5b0ca3e2c5',t:'MTc3OTg0MDAyOQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();